llm-sandbox
LLM Sandbox is a lightweight and portable sandbox environment designed to run large language model (LLM) generated code in a safe and isolated mode.
Decision gist · record as of 2026-08-14
Yes, with conditions. Install if you need to safely execute LLM-generated code and have a container backend (Docker, Kubernetes, or Podman) available. The package is actively maintained, has low install friction, and integrates well with popular LLM frameworks. However, verify the unclear license terms before use in proprietary contexts, and ensure your deployment environment supports containerization.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a container backend (Docker, Kubernetes, or Podman) installed and running on the host system; Python 3.10 or later.
- Low friction installation with a single runtime dependency (pydantic).
- Active maintenance with recent releases; last commit 2026-08-14 and 1104 repository stars indicate ongoing development.
License · maintenance · safety
(unclear) — License status is unclear—no SPDX identifier or raw license text is available in the package metadata. Verify the actual license terms in the repository before adopting in proprietary or restricted-use contexts.
last release 2026-08-03 (11 days) · last repo commit 2026-08-14 · 1,104 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 661,557 downloads/mo, #5,448 on PyPI
Alternatives
Verify before relying
pip install llm-sandbox
from llm_sandbox import SandboxSession
with SandboxSession(lang="python") as session:
result = session.run('print("Hello from sandbox")')
print(result.stdout)- Whether the unclear license permits commercial or proprietary use without restrictions.
- Performance overhead and startup time for container initialization in production workflows.
- Security audit status and third-party validation of the isolation guarantees claimed.
What it is and what it does
LLM Sandbox is a lightweight execution environment that runs code generated by large language models inside isolated containers, protecting the host system from untrusted or malicious code. It abstracts away container orchestration details and provides a unified Python API for spawning sandboxed sessions across multiple backends (Docker, Kubernetes, Podman) and programming languages (Python, JavaScript, Java, C++, Go, R).
The package is designed to integrate with LLM agent frameworks and Claude Desktop via the Model Context Protocol. It handles dependency installation, resource limits (CPU, memory, execution time), network isolation, and artifact extraction (plots, visualizations). Interactive sessions maintain state across multiple code runs, simulating notebook-style workflows. The single runtime dependency is pydantic, and it supports Python 3.10 through 3.13.
Use it for
- Run code snippets generated by Claude, ChatGPT, or other LLMs without exposing your system to untrusted execution.
- Execute multi-language code (Python, JavaScript, Java, C++, Go, R) in a unified interface for polyglot AI agent workflows.
- Extract and save plots and visualizations generated by sandboxed Python or R code automatically.
- Deploy scalable code execution for AI agent frameworks (LangChain, CrewAI, Pydantic AI, etc.) with resource limits and security policies.
- Maintain state across multiple code executions in interactive Python sessions without manual serialization.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
Install if you need to safely execute LLM-generated code and have a container backend (Docker, Kubernetes, or Podman) available. The package is actively maintained, has low install friction, and integrates well with popular LLM frameworks. However, verify the unclear license terms before use in proprietary contexts, and ensure your deployment environment supports containerization.
Install
llm-sandbox on PyPI
Before you install
Low friction installation with a single runtime dependency (pydantic). Active maintenance with recent releases; last commit 2026-08-14 and 1104 repository stars indicate ongoing development.
Requires a container backend (Docker, Kubernetes, or Podman) installed and running on the host system; Python 3.10 or later.
License in practice
License status is unclear—no SPDX identifier or raw license text is available in the package metadata. Verify the actual license terms in the repository before adopting in proprietary or restricted-use contexts.
Quickstart
pip install llm-sandbox
from llm_sandbox import SandboxSession
with SandboxSession(lang="python") as session:
result = session.run('print("Hello from sandbox")')
print(result.stdout)Verify before relying
- Whether the unclear license permits commercial or proprietary use without restrictions.
- Performance overhead and startup time for container initialization in production workflows.
- Security audit status and third-party validation of the isolation guarantees claimed.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagepydantic |
| Maintenance | Actively maintained 11 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 661,557 / month, #5,448 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Software Development :: Libraries :: Python Modules |
Evidence: llm_sandbox-0.3.44-py3-none-any.whl
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See also cwsandbox · e2b-code-interpreter · e2b · hyperlight-sandbox · contree-sdk · langchain-daytona · k8s-agent-sandbox · langchain-azure-dynamic-sessions · pydantic-ai-backend · deepagents-code